Report on the suitability of the actual reference data sets for deep Argo DMQC

This report provides an assessment of the availability and quality of the CTD reference data for Argo for the regions of deployments of the deep European Argo fleet

Report of the outcome of the comparative study for the deep Argo quality control processing

Report of the most appropriate methods and tools in the quality control of deep Argo float

Bio-Optical Sensors on Argo Floats

The general objective of the IOCCG BIO-Argo working group is to elaborate recommendations for establishing a framework for the future development of a cost-effective, bio-optical float network corresponding to the needs and expectations of the scientific community. In this context, our recommendations will necessarily be broad; they range from the identification of key bio-optical measurements to be implemented on floats, to the real-time management of the data flux resulting from the deployment of a "fleet of floats". Each chapter of this report is dedicated to an essential brick leading towards the goal of implementing a bio-optical profiling float network. The following topics are discussed in the Chapters listed below: - Chapter 2 reviews the scientific objectives that could be tackled through the development of such networks, by allowing some of the gaps in the present spatio-temporal resolution of bio-optical variables to be progressively filled. - Chapter 3 identifies the optical and bio-optical properties that are now amenable to remote and autonomous measurement through the use of optical sensors mounted on floats. - Chapter 4 addresses the question of sensor requirements, in particular with respect to measurements performed from floats. - Chapter 5 proposes and argues for the development of dedicated float missions corresponding to specific scientific objectives and relying on specific optical sensor suites, as well as on specific modes of float operation. - Chapter 6 identifies technological issues that need to be addressed for the various bio-optical float missions to become even more cost-effective. - Chapter 7 covers all aspects of data treatment ranging from the development of various quality control procedures (from real-time to delayed mode) to the architecture required for favoring easy access to data. - Chapter 8 reviews the necessary steps and experience required before the operational implementation of different types of float networks can become a reality.JRC.H.5-Land Resources Managemen

From SeaDataNet to SeaDataCloud: historical data collections and new data products

Temperature and Salinity historical data collections covering the time period 1900-2013/2014 were created for each European marginal sea (Arctic Sea, Baltic Sea, Black Sea, North Sea, North Atlantic Ocean, and Mediterranean Sea) within the framework of SeaDataNet2 Project and they are available as ODV collections through a web catalog (https://www.seadatanet.org/Products/Aggregated-datasets). Two versions have been published and they represent a snapshot of the SeaDataNet database content at two different times: V1.1 (January 2014) and V2 (March 2015). A Quality Control Strategy (QCS) was developped and continuously refined in order to improve the quality of the database content and create the best data products. The QCS consists of four main phases: 1) data harvesting from the data infrastructure; 2) file and parameter aggregation; 3) secondary quality check analysis; 4) correction of data anomalies. The approach is iterative to facilitate the upgrade of the database content and it allows a versioning of data products. Regional temperature and salinity monthly climatologies have been produced from V1.1 historical data collections and they are also available (https://www.seadatanet.org/Products/Climatologies). Within the new SeaDataCloud Project the release of updated historical data collections and new climatologies is planned. SeaDataCloud novelties are the introduction of decadal climatologies at various resolutions, the development of climatologies for the Global Ocean and a task dedicated to new data products, like Mixed Layer Depth climatologies, Ocean Heat Content estimates, coastal climatologies from HF radar data. All SeaDataCloud products are available through a dedicated web catalogue together with their relative Digital Object Identifier (DOI) and Product Information Document (PIDoc) containing all specifications about product’s generation, quality assessment and technical details to facilitate users’ uptake. The presentation will briefly overview the existing SeaDataNet products and introduce the SeaDataCloud products’ plan, but the main focus will be on the first release (February 2018) of SeaDataCloud Temperature and Salinity historical data collections, spanning the time period 1900-2017, their characteristics in terms of space-time data distribution and their usability.SeaDataCloud ProjectPublishedVienna4A. Oceanografia e clim

SeaDataNet regional climatologies: an overview

In the frame of the SeaDataNet project, several regional climatologies for the temperature and salinity are being developed by different groups. The data used for these climatologies are distributed by the 40 SeaDataNet data centers. Such climatologies have several uses: 1. the detection of outliers by comparison of the in situ data with the climatological fields, 2. the the optimization of locations of new observations, 3. the initialization of numerical hydrodynamic models. 4. definition of a reference state to identify anomalies and to detect long-term climatic trends Diva (Data Interpolating Variational Analysis) software is adapted to each region by taking into account the geometrical characteristics (coastlines, bathymetry) and the distribution of data (correlation length, signal-to-noise ratio, reference field). The regional climatologies treated in this work are: - JRA5: North Atlantic - JRA6: Mediterranean Sea - JRA7: Baltic Sea - JRA8: North Sea, Arctic Sea Several examples of gridded fields are presented in this work. The validation of the different products is carried out through a comparison with the last release of the widespread World Ocean Atlas 2005

DMQC cookbook for core Argo parameters

This cookbook is to document the end-to-end processing chain of Delayed Mode Quality Control (DMQC) of Core Argo parameters. It provides guidelines on existing manuals, and explains best practices through case studies. This document was initiated after the 1st EU DMQC workshop held in Brest in April 2018, under the MOCCA project. Lately, this work has been undertaken under EuroArgo RISE project. The document is organized as follows. The first part gives some general information (e.g.: How to check quality indicators in delayed mode? What are the reference databases? How to correct pressure? How to use the OWC software to correct salinity? What are the common failures? etc.). The second part gives more specific information for the regional analysis (specific difficulties encountered, reference data available in regional seas, configuration parameters usually used, etc...). The regions covered so far are: the sub-polar Atlantic zone, the Nordic Seas, the Mediterranean and Black Seas, and the Southern Ocean. The third part of the cookbook presents detailed examples of delayed-mode processing for float data in these regions

SeaDataCloud Data Products for the European marginal seas and the Global Ocean

Data products, based on in situ temperature and salinity observations from SeaDataNet infrastructure, have been released within the framework of SeaDataCloud (SDC) project. The data from different data providers are integrated and harmonized thanks to standardized quality assurance and quality control methodologies conducted at various stages of the data value chain. The data ingested within SeaDataNet are earlier validated by data providers who assign corresponding quality flags, but a Quality Assurance Strategy has been implemented and progressively refined to guarantee the consistency of the database content and high quality derived products. Two versions of aggregated datasets for the European marginal seas have been published and used to compute regional high resolution climatologies. External datasets, the World Ocean Database from NOAA and the CORA dataset from the Copernicus Marine Service in situ Thematic Assembly Center, have been integrated with SDC data collections to maximize data coverage and minimize the mapping error. The products are available through the SDC catalogue accompanied by Product Information Documents containing the specifications about product’s generation, characteristics and usability. Digital Object Identifiers are assigned to products and relative documentation to foster transparency of the production chain, acknowledging all actors involved from data providers to information producers

Impact of ocean carbon system variability on the detection of temporal increases in anthropogenic CO2

Author Posting. © American Geophysical Union, 2008. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 113 (2008): C03019, doi:10.1029/2007JC004153.Estimates of temporal trends in oceanic anthropogenic carbon dioxide (CO2) rely on the ability of empirical methods to remove the large natural variability of the ocean carbon system. A coupled carbon-climate model is used to evaluate these empirical methods. Both the ΔC* and multiple linear regression (MLR) techniques reproduce the predicted increase in dissolved inorganic carbon for the majority of the ocean and have similar average percent errors for decadal differences (24.1% and 25.5%, respectively). However, this study identifies several regions where these methods may introduce errors. Of particular note are mode and deep water formation regions, where changes in air-sea disequilibrium and structure in the MLR residuals introduce errors. These results have significant implications for decadal repeat hydrography programs, indicating the need for subannual sampling in certain regions of the oceans in order to better constrain the natural variability in the system and to robustly estimate the intrusion of anthropogenic CO2.We would like to acknowledge funding from NSF (OCE02-23869), NCAR, the WHOI Ocean Climate Institute, a Linden Earth Systems Graduate Fellowship (MIT), and a National Defense Science and Engineering Graduate Fellowship. NCAR is sponsored by the National Science Foundation. R.W. is supported by the Office of Oceanic and Atmospheric Research at NOAA